[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290388-105":3,"doc-detail-290388-en":81,"detail-sidebar-cat-0-en-105":98},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","advancing-network-intrusion-detection-systems-through-machine-learning-algorithms-msc-research-project","Advancing Network Intrusion Detection Systems through Machine Learning Algorithms - MSc Research Project","","The configuration manual for an MSc Data Analytics research project documents the complete system setup, tools, and algorithms used to advance a machine learning–based intrusion detection system. The study builds IDS models including SVM, Random Forest, KNN, a hybrid deep learning architecture, and a CNN-GRU-BiLSTM model. Feature dimensionality is reduced using RFE and PCA, and performance is validated with confusion matrices, evaluation metrics, and ROC curves. It also specifies the Windows 11 hardware baseline and RStudio implementation, and uses the NSL-KDD dataset from Kaggle with preprocessing, labeling, and SMOTE-based balancing.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/advancing-network-intrusion-detection-systems-through-machine-learning-algorithms-msc-research-project/290388/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/advancing-network-intrusion-detection-systems-through-machine-learning-algorithms-msc-research-project/290388.png","ImageObject",300,407,{"name":42,"@type":43},"Eliana","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-30","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",17,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What machine learning models are used in the intrusion detection study?","Question",{"text":63,"@type":64},"The study uses SVM, Random Forest, KNN, Isolation forest, and a deep learning approach including CNN-GRU-BiLSTM (and related CNN-GRU-LSTM notation) across different scenarios.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How is feature dimensionality handled to improve model performance?",{"text":68,"@type":64},"Dimensionality is reduced using Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA), and models are trained both with and without these reduction methods.",{"name":70,"@type":61,"acceptedAnswer":71},"How is model evaluation performed and what dataset is used?",{"text":72,"@type":64},"Performance is validated using confusion matrices, evaluation matrices, and ROC curves. The dataset is NSL-KDD downloaded from Kaggle, with preprocessing and label modification steps and SMOTE to balance classes.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290388,1789641539,{"code":4,"msg":82,"data":83},"success",{"doc_id":79,"user_id":84,"nickname":42,"user_avatar":85,"doc_module":4,"category_id":86,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":87,"file_id":88,"file_url":89,"file_type":90,"file_size":91,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":92,"language":93,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":94,"faqs":95,"seo_title":96,"seo_description":12,"update_tm":80,"read_time":97,"preview_page_count":4},4398048949847,"https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Configuration Manual  \nMSc Research Project MSc Data Analytics 2023-2024  \nArya Eldho Student ID: 22248056  \nSchool of Computing National College of Ireland  \nSupervisor: Jorge Basilio  \nNational College of Ireland MSc Project Submission Sheet School of Computing  \nStudent Name:   ARYA ELDHO    \nStudent ID:   22248056   Programme:   MSC DATA ANALYTICS   Year:   2023-2024.  \nModule:   MSC RESEARCH PROJECT    \nLecturer:   JORGE BASILIO    \nSubmission  \nDue Date:   12 AUGUST 2024    \nProject Title:   ADVANCING NETWORK INTRUSION DETECTION SYSTEMS THROUGH MACHINE LEARNING ALGORITHMS    \npage Count: ………12………………….  \nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author's written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \nSignature:   ARYA ELDHO    \nDate:   12 AUGUST 2024    \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both for your own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator Office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nConfiguration Manual  \nArya Eldho  \nStudent ID: 22248056  \n1 Introduction  \nThe configuration manual provides the complete details of the system configuration, tools and algorithms used for the advancement of network intrusion detection systems using machine learning algorithms. In the research study a machine learning based IDS is built. The machine learning models:SVM, Random Forest(RF), KNN and a hybrid deep learning model and the Convolutional Neural Network-Gated Recurrent Unit-Bidirectional Long Short Term Memory(CNN-GRU-BiLSTM) models are used in the study. The dimensionality of the features in the dataset is reduced using both Recursive Feature Elimination (RFE) and Principal Component Analysis(PCA) . To validate the performance of the models, confusion matrix, evaluation matrix and ROC curves were used. The process analyzed during the development phase and all the research findings are documented in the following sections.  \n2. System Specification  \nThe hardware and software requirements are mentioned in the following section  \n2.1 Hardware Requirements  \n\n| Operating system | Windows 11 |\n| --- | --- |\n| RAM | 16 GB |\n| Hard Disk | 250 GB |\n\n2.2 Software Requirements  \nThe research work was implemented using machine learning models, and hence R studio is used for the implementation.  \n3. Data Source  \nThe dataset used for the study is NSL-KDD dataset taken from the Kaggle. The data source was very useful in evaluating network intrusions. The dataset is downloaded from the given source link :  \n[https://www.kaggle.com/datasets/hassan06/nslkdd](https://www.kaggle.com/datasets/hassan06/nslkdd)  \n4. Data Load And Analysis  \nFirst step was to import all the necessary libraries as shown in fig 1.  \nFig 1: Importing libraries  \nNext process is to load the train and test data from the CSV and to assign the column names as‘feature’  \nFig 2: feature name definition and data loading  \n5. Data Processing  \nThis section explains the preprocessing of the dataset. Here the dataset summary is driven, dropping the colum","cbCaimqQkmO0YyrM","https://ap.wps.com/l/cbCaimqQkmO0YyrM","pdf",642539,12,"English","# 1 Introduction\n# 2 System Specification\n## 2.1 Hardware Requirements\n## 2.2 Software Requirements\n# 3 Data Source\n# 4 Data Load And Analysis\n# 5 Data Processing\n# 6 Model Building And Evalutaion\n## 6.1 Model training without feature dimensionality reduction methods\n## 6.2 Model training with PCA\n## 6.3 Models with RFE","[{\"question\":\"What machine learning models are used in the intrusion detection study?\",\"answer\":\"The study uses SVM, Random Forest, KNN, Isolation forest, and a deep learning approach including CNN-GRU-BiLSTM (and related CNN-GRU-LSTM notation) across different scenarios.\"},{\"question\":\"How is feature dimensionality handled to improve model performance?\",\"answer\":\"Dimensionality is reduced using Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA), and models are trained both with and without these reduction methods.\"},{\"question\":\"How is model evaluation performed and what dataset is used?\",\"answer\":\"Performance is validated using confusion matrices, evaluation matrices, and ROC curves. The dataset is NSL-KDD downloaded from Kaggle, with preprocessing and label modification steps and SMOTE to balance classes.\"}]","Advancing Network Intrusion Detection Systems through Machine Learning Algorithms - MSc Research Project | PDF",30,{"code":4,"msg":82,"data":99},[100,104,108,112,117,122,127,129,134,137,141],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":101,"show_sort_weight":102,"slug":103},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":105,"show_sort_weight":106,"slug":107},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":109,"show_sort_weight":110,"slug":111},"Exam",70,"exam",{"id":113,"doc_module":4,"doc_module_name":25,"category_name":114,"show_sort_weight":115,"slug":116},5,"Comic",60,"comic",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":119,"show_sort_weight":120,"slug":121},6,"Technology",50,"technology",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":125,"slug":126},7,"Healthcare",40,"healthcare",{"id":86,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":97,"slug":128},"research-report",{"id":130,"doc_module":4,"doc_module_name":25,"category_name":131,"show_sort_weight":132,"slug":133},9,"Religion & Spirituality",20,"religion-spirituality",{"id":132,"doc_module":4,"doc_module_name":25,"category_name":135,"show_sort_weight":132,"slug":136},"World Cup","world-cup",{"id":138,"doc_module":4,"doc_module_name":25,"category_name":139,"show_sort_weight":138,"slug":140},10,"Lifestyle","lifestyle",{"id":142,"doc_module":4,"doc_module_name":25,"category_name":143,"show_sort_weight":113,"slug":144},19,"General","general"]